# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig


def 	lowerCamelCase					(	UpperCamelCase     :				List[str]			,       UpperCamelCase     :				List[str]			,       UpperCamelCase     :				Optional[int]=0							)   ->    Any:
		# Format the message.
		if name is None:
				_lowerCamelCase				   =  None
		else:
				_lowerCamelCase				   =  '.' * max(0			,       spaces - 2							) + '# {:' + str(50 - spaces							) + 's}'
				_lowerCamelCase				   =  fmt.format(UpperCamelCase							)

		# Print and recurse (if needed).
		if isinstance(UpperCamelCase			,       UpperCamelCase							):
				if msg is not None:
						print(UpperCamelCase							)
				for k in val.keys():
						recursive_print(UpperCamelCase			,       val[k]			,       spaces + 2							)
		elif isinstance(UpperCamelCase			,       torch.Tensor							):
				print(UpperCamelCase			,       ':'			,       val.size()							)
		else:
				print(UpperCamelCase			,       ':'			,       UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				List[str]			,       UpperCamelCase     :				str			,       UpperCamelCase     :				Optional[int]			,       UpperCamelCase     :				Any			,       UpperCamelCase     :				Tuple							)   ->    List[str]:
		# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
		# for compatibility with later versions of NVIDIA Megatron-LM.
		# The inverse operation is performed inside Megatron-LM to read checkpoints:
		# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
		# If param is the weight tensor of the self-attention block, the returned tensor
		# will have to be transposed one more time to be read by HuggingFace GPT2.
		_lowerCamelCase				   =  param.size()
		if checkpoint_version == 1.0:
				# version 1.0 stores [num_heads * hidden_size * num_splits, :]
				_lowerCamelCase				   =  (num_heads, hidden_size, num_splits) + input_shape[1:]
				_lowerCamelCase				   =  param.view(*UpperCamelCase							)
				_lowerCamelCase				   =  param.transpose(0			,       2							)
				_lowerCamelCase				   =  param.transpose(1			,       2							).contiguous()
		elif checkpoint_version >= 2.0:
				# other versions store [num_heads * num_splits * hidden_size, :]
				_lowerCamelCase				   =  (num_heads, num_splits, hidden_size) + input_shape[1:]
				_lowerCamelCase				   =  param.view(*UpperCamelCase							)
				_lowerCamelCase				   =  param.transpose(0			,       1							).contiguous()
		_lowerCamelCase				   =  param.view(*UpperCamelCase							)
		return param


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				Optional[int]			,       UpperCamelCase     :				Any							)   ->    Union[str, Any]:
		# The converted output model.
		_lowerCamelCase				   =  {}

		# old versions did not store training args
		_lowerCamelCase				   =  input_state_dict.get('args'			,       UpperCamelCase							)
		if ds_args is not None:
				# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
				# from pprint import pprint
				# pprint(vars(ds_args))

				_lowerCamelCase				   =  ds_args.padded_vocab_size
				_lowerCamelCase				   =  ds_args.max_position_embeddings
				_lowerCamelCase				   =  ds_args.hidden_size
				_lowerCamelCase				   =  ds_args.num_layers
				_lowerCamelCase				   =  ds_args.num_attention_heads
				_lowerCamelCase				   =  ds_args.ffn_hidden_size
				# pprint(config)

		# The number of heads.
		_lowerCamelCase				   =  config.n_head
		# The hidden_size per head.
		_lowerCamelCase				   =  config.n_embd // config.n_head
		# Megatron-LM checkpoint version
		if "checkpoint_version" in input_state_dict.keys():
				_lowerCamelCase				   =  input_state_dict['checkpoint_version']
		else:
				_lowerCamelCase				   =  0.0

		# The model.
		_lowerCamelCase				   =  input_state_dict['model']
		# The language model.
		_lowerCamelCase				   =  model['language_model']
		# The embeddings.
		_lowerCamelCase				   =  lm['embedding']

		# The word embeddings.
		_lowerCamelCase				   =  embeddings['word_embeddings']['weight']
		# Truncate the embedding table to vocab_size rows.
		_lowerCamelCase				   =  word_embeddings[: config.vocab_size, :]
		_lowerCamelCase				   =  word_embeddings

		# The position embeddings.
		_lowerCamelCase				   =  embeddings['position_embeddings']['weight']
		# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
		_lowerCamelCase				   =  pos_embeddings.size(0							)
		if n_positions != config.n_positions:
				raise ValueError(
				    F"""pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match"""							)
		# Store the position embeddings.
		_lowerCamelCase				   =  pos_embeddings

		# The transformer.
		_lowerCamelCase				   =  lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

		# The regex to extract layer names.
		_lowerCamelCase				   =  re.compile(R'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'							)

		# The simple map of names for "automated" rules.
		_lowerCamelCase				   =  {
		    'attention.dense': '.attn.c_proj.',
		    'self_attention.dense': '.attn.c_proj.',
		    'mlp.dense_h_to_4h': '.mlp.c_fc.',
		    'mlp.dense_4h_to_h': '.mlp.c_proj.',
		}

		# Extract the layers.
		for key, val in transformer.items():
				# Match the name.
				_lowerCamelCase				   =  layer_re.match(UpperCamelCase							)

				# Stop if that's not a layer
				if m is None:
						break

				# The index of the layer.
				_lowerCamelCase				   =  int(m.group(1							)							)
				# The name of the operation.
				_lowerCamelCase				   =  m.group(2							)
				# Is it a weight or a bias?
				_lowerCamelCase				   =  m.group(3							)

				# The name of the layer.
				_lowerCamelCase				   =  F"""transformer.h.{layer_idx}"""

				# For layernorm(s), simply store the layer norm.
				if op_name.endswith('layernorm'							):
						_lowerCamelCase				   =  'ln_1' if op_name.startswith('input'							) else 'ln_2'
						_lowerCamelCase				   =  val

				# Transpose the QKV matrix.
				elif (
				    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
				) and weight_or_bias == "weight":
						# Insert a tensor of 1x1xDxD bias.
						_lowerCamelCase				   =  torch.tril(torch.ones((n_positions, n_positions)			,       dtype=torch.floataa							)							).view(
						    1			,       1			,       UpperCamelCase			,       UpperCamelCase							)
						_lowerCamelCase				   =  causal_mask

						# Insert a "dummy" tensor for masked_bias.
						_lowerCamelCase				   =  torch.tensor(-1e4			,       dtype=torch.floataa							)
						_lowerCamelCase				   =  masked_bias

						_lowerCamelCase				   =  fix_query_key_value_ordering(UpperCamelCase			,       UpperCamelCase			,       3			,       UpperCamelCase			,       UpperCamelCase							)
						# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
						_lowerCamelCase				   =  out_val.transpose(0			,       1							).contiguous()
						# Store.
						_lowerCamelCase				   =  out_val

				# Transpose the bias.
				elif (
				    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
				) and weight_or_bias == "bias":
						_lowerCamelCase				   =  fix_query_key_value_ordering(UpperCamelCase			,       UpperCamelCase			,       3			,       UpperCamelCase			,       UpperCamelCase							)
						# Store. No change of shape.
						_lowerCamelCase				   =  out_val

				# Transpose the weights.
				elif weight_or_bias == "weight":
						_lowerCamelCase				   =  megatron_to_transformers[op_name]
						_lowerCamelCase				   =  val.transpose(0			,       1							)

				# Copy the bias.
				elif weight_or_bias == "bias":
						_lowerCamelCase				   =  megatron_to_transformers[op_name]
						_lowerCamelCase				   =  val

    # DEBUG.
		assert config.n_layer == layer_idx + 1

		# The final layernorm.
		_lowerCamelCase				   =  transformer['final_layernorm.weight']
		_lowerCamelCase				   =  transformer['final_layernorm.bias']

		# For LM head, transformers' wants the matrix to weight embeddings.
		_lowerCamelCase				   =  word_embeddings

		# It should be done!
		return output_state_dict


def 	lowerCamelCase					(	)   ->    Any:
		# Create the argument parser.
		_lowerCamelCase				   =  argparse.ArgumentParser()
		parser.add_argument('--print-checkpoint-structure'			,       action='store_true'							)
		parser.add_argument(
		    'path_to_checkpoint'			,       type=UpperCamelCase			,       help='Path to the checkpoint file (.zip archive or direct .pt file)'			,       )
		parser.add_argument(
		    '--config_file'			,       default=''			,       type=UpperCamelCase			,       help='An optional config json file describing the pre-trained model.'			,       )
		_lowerCamelCase				   =  parser.parse_args()

		# Extract the basename.
		_lowerCamelCase				   =  os.path.dirname(args.path_to_checkpoint							)

		# Load the model.
		# the .zip is very optional, let's keep it for backward compatibility
		print(F"""Extracting PyTorch state dictionary from {args.path_to_checkpoint}"""							)
		if args.path_to_checkpoint.endswith('.zip'							):
				with zipfile.ZipFile(args.path_to_checkpoint			,       'r'							) as checkpoint:
						with checkpoint.open('release/mp_rank_00/model_optim_rng.pt'							) as pytorch_dict:
								_lowerCamelCase				   =  torch.load(UpperCamelCase			,       map_location='cpu'							)
		else:
				_lowerCamelCase				   =  torch.load(args.path_to_checkpoint			,       map_location='cpu'							)

		_lowerCamelCase				   =  input_state_dict.get('args'			,       UpperCamelCase							)

		# Read the config, or default to the model released by NVIDIA.
		if args.config_file == "":
				if ds_args is not None:
						if ds_args.bias_gelu_fusion:
								_lowerCamelCase				   =  'gelu_fast'
						elif ds_args.openai_gelu:
								_lowerCamelCase				   =  'gelu_new'
						else:
								_lowerCamelCase				   =  'gelu'
				else:
						# in the very early days this used to be "gelu_new"
						_lowerCamelCase				   =  'gelu_new'

				# Spell out all parameters in case the defaults change.
				_lowerCamelCase				   =  GPTaConfig(
				    vocab_size=5_02_57			,       n_positions=10_24			,       n_embd=10_24			,       n_layer=24			,       n_head=16			,       n_inner=40_96			,       activation_function=UpperCamelCase			,       resid_pdrop=0.1			,       embd_pdrop=0.1			,       attn_pdrop=0.1			,       layer_norm_epsilon=1e-5			,       initializer_range=0.02			,       summary_type='cls_index'			,       summary_use_proj=UpperCamelCase			,       summary_activation=UpperCamelCase			,       summary_proj_to_labels=UpperCamelCase			,       summary_first_dropout=0.1			,       scale_attn_weights=UpperCamelCase			,       use_cache=UpperCamelCase			,       bos_token_id=5_02_56			,       eos_token_id=5_02_56			,       )
		else:
				_lowerCamelCase				   =  GPTaConfig.from_json_file(args.config_file							)

		_lowerCamelCase				   =  ['GPT2LMHeadModel']

		# Convert.
		print('Converting'							)
		_lowerCamelCase				   =  convert_megatron_checkpoint(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)

		# Print the structure of converted state dict.
		if args.print_checkpoint_structure:
				recursive_print(UpperCamelCase			,       UpperCamelCase							)

		# Add tokenizer class info to config
		# see https://github.com/huggingface/transformers/issues/13906)
		if ds_args is not None:
				_lowerCamelCase				   =  ds_args.tokenizer_type
				if tokenizer_type == "GPT2BPETokenizer":
						_lowerCamelCase				   =  'gpt2'
				elif tokenizer_type == "PretrainedFromHF":
						_lowerCamelCase				   =  ds_args.tokenizer_name_or_path
				else:
						raise ValueError(F"""Unrecognized tokenizer_type {tokenizer_type}"""							)
		else:
				_lowerCamelCase				   =  'gpt2'

		_lowerCamelCase				   =  AutoTokenizer.from_pretrained(UpperCamelCase							)
		_lowerCamelCase				   =  type(UpperCamelCase							).__name__
		_lowerCamelCase				   =  tokenizer_class

		# Store the config to file.
		print('Saving config'							)
		config.save_pretrained(UpperCamelCase							)

		# Save tokenizer based on args
		print(F"""Adding {tokenizer_class} tokenizer files"""							)
		tokenizer.save_pretrained(UpperCamelCase							)

		# Store the state_dict to file.
		_lowerCamelCase				   =  os.path.join(UpperCamelCase			,       'pytorch_model.bin'							)
		print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""							)
		torch.save(UpperCamelCase			,       UpperCamelCase							)


####################################################################################################

if __name__ == "__main__":
				main()

####################################################################################################
def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    float:
		return base * power(UpperCamelCase			,       (exponent - 1)							) if exponent else 1


if __name__ == "__main__":
				print('Raise base to the power of exponent using recursion...')
				A						=			int(input('Enter the base: ').strip())
				A						=			int(input('Enter the exponent: ').strip())
				A						=			power(base, abs(exponent))
				if exponent < 0:  # power() does not properly deal w/ negative exponents
								A						=			1 / result
				print(F'''{base} to the power of {exponent} is {result}''')